Understanding AI/Machine Learning + Process Automation

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Whitepaper

Creating the Data Ecosystem for Your Transition to an AI World

When Alan Turing was developing the Turing Test for determining machine
intelligence — what we today refer to as Artificial Intelligence (AI) — he first posed
the question, “Can machines think?” At the time, this seemed preposterous, but
technology advances in recent years now have us asking a similar question:
“Can machines learn?” The answer is “yes,” as long as we supply them with
enough of the right kind of data.

This ability to learn is a specific, data-oriented form of AI that has the power to dramatically
increase productivity and streamline business processes. Companies are increasingly looking
to AI and specifically machine learning (ML) as a way to improve their businesses. While many of these
applications are still in the early developmental phases, companies must prepare now in order to
successfully implement AI and ML in the future and take full advantage of the power of this technology.
Specifically, organizations need to establish the data ecosystems today that will become the foundation
of ML applications, training models and business process development in the future.

This white paper is the second in a two-part series on the impact of AI on process automation. In the first,
we examined this impact at a high level. In this paper, we take a closer look at ML specifically, and the
actions organizations need to take today to prepare for a high-velocity AI-driven world.

Understanding AI/ML and BPM

AI is a broad term that has been applied
to everything from robots to self-driving
cars to personal digital assistants such
as Alexa. AI also promises great productivity
gains for companies by integrating natural
language processing and ML into business
applications. Application developers are using
the natural language processing features of AI
to streamline human interaction with the machine
through natural language interfaces
and language translation.

With AI, organizations are able to streamline their
business processes by automating routine tasks,
improving user interfaces and quickly analyzing
large amounts of data. Adding these capabilities
to process automation helps reduce the amount
of time spent on certain tasks, improving
customer experience and reducing costs. Once
fully implemented, the technology ultimately will
enhance the K2 low-code approach to solving
process automation issues.

Businesses are also implementing AI to automate
tasks, specific decisions or even higher-level
workflows, such as typical team assignments for
various projects. These automated applications
rely on machine learning to focus more on
predictive data analysis and use algorithms that
learn from data without relying on discrete rulesbased
programming. This allows the machine
to use the inference results from data patterns
to direct an action that fits the true nature of the
business as its been executing over time, instead
of relying on discrete, hard-coded and possibly
outdated rules.

While AI is gaining acceptance in some industries,
many companies remain reluctant to adopt it as
part of their business plans. Data science experts
remain a rare — and expensive — commodity and
most companies lack the financial resources to
hire them. In many cases, the value and return
on investment of these technologies remain
untested and corporate executives hesitate to
invest in an unproven technology with so many
competing initiatives (e.g. cloud computing,
cybersecurity). As a result, the leading adopters
tend to be from the hi-tech, telecom and
financial services sectors where fierce
competition drives organizations to continuously
seek new innovations that give them
a competitive advantage.

Even so, no one doubts that AI and ML are
the future of enterprise computing. This future,
however, is coming fast and competitive
advantage could depend on being ready
sooner than your peers. Companies must begin
preparing now so they will be ready to take
full advantage of the increased productivity,
improved decision-making processes and
new ways to serve customers.

Laying the Foundation

Machine learning analyzes data flow in the
enterprise. Anticipating this eventual capability,
the most critical issue is how companies prepare
the data that will be used as the basis for machine
learning. Data scrubbing and formatting is one
of the greatest challenges in implementing AI
so companies should adopt clean data format
methods now.

To address this challenge, IT and the lines
of business need to organize around a specific
business problem they are trying to solve.
Defining that specific problem creates a direct
correlation between the input, its format and
shape, and ultimately the decision output.
Therefore, understanding the outcome and
the kinds of decisions that need to be made
will determine which data sets you use and
how you need to scrub, transform or clean them.

While you need clean data to populate
an AI application, it should not be too perfect.
This might seem counterintuitive, but at the
core, AI is just algorithms that adjust their own
inputs through weights as they learn over time.
So, actual machine “learning” results from
understanding and processing big sets of realworld
data. This is typically achieved through
what is called supervised learning. Explained on
a high level, you take a large, clean, real-world
data set and feed 80 percent of the data through
the ML network, and then use the remaining 20
percent to gauge the accuracy of the predictions.

Finding the Low-Hanging Fruit

Companies need to look for opportunities
where AI can make a difference and identify
the most valuable use cases for AI. This could
be considered the “low-hanging fruit” in an
organization—processes that would benefit the
most from automation and where good, clean
data might be readily accessible.

The next step is building a solid technical
infrastructure and investing in developing the
digital assets for collecting and organizing
the data needed for AI and machine learning.
Businesses also need to integrate technology into
workplace processes and develop a workforce
culture that optimizes the interaction between
humans and machines.

Finally, organizations need a common platform
that allows IT and the lines of business to
optimize the data that will be used to build the
training models for ML. For example, companies
using K2 are able to make process decisions
more effectively (because they are based
on historical data). The process metrics and
reporting that the K2 platform provides can
also be used as inputs to help make the ML
algorithms learn even faster, often resulting in
quick wins.

Companies that currently use K2 are in the best position
to take advantage of emerging ML technologies.

AI and machine learning are poised to transform process automation. More decisions
can be automated based on real business execution trends, freeing staff to focus on
strategic efforts or direct customer service. Enterprises need to take steps now to ensure
they have the systems in place to take full advantage of the technology’s ability to
improve productivity and streamline processes.